pdf viewer library c# : How to copy an image from a pdf application SDK tool html wpf web page online TextMiningO2-part1801

Data Science with R
Hands-On
Text Mining
6 Distribution of Term Frequencies
# Frequency of frequencies.
head(table(freq), 15)
## freq
##
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
## 2381 1030 503 311 210 188 134 130
82
83
65
61
54
52
51
tail(table(freq), 15)
## freq
## 483 544 547 555 578 609 611 616 703 709 776 887 1366 1446 3101
##
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
So we can see here that there are 2381 terms that occur just once.
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Data Science with R
Hands-On
Text Mining
7 Conversion to Matrix and Save to CSV
We can convert the document term matrix to a simple matrix for writing to a CSV le, for
example, for loading the data into other software if we need to do so. To write to CSV we rst
convert the data structure into a simple matrix:
<- as.matrix(dtm)
dim(m)
## [1]
46 6508
For very large corpus the size of the matrix can exceed R’s calculation limits. This will manifest
itself as a integer over ow error with a message like:
## Error in vector(typeof(x$v), nr * nc) : vector size cannot be NA
## In addition: Warning message:
## In nr * nc : NAs produced by integer overflow
If this occurs, then consider removing sparse terms from the document term matrix, as we discuss
shortly.
Once converted into a standard matrix the usual write.csv() can be used to write the data to
le.
write.csv(m, file="dtm.csv")
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Data Science with R
Hands-On
Text Mining
8 Removing Sparse Terms
We are often not interested in infrequent terms in our documents. Such \sparse" terms can be
removed from the document term matrix quite easily using removeSparseTerms():
dim(dtm)
## [1]
46 6508
dtms <- removeSparseTerms(dtm, 0.1)
dim(dtms)
## [1] 46 6
This has removed most terms!
inspect(dtms)
## <<DocumentTermMatrix (documents: 46, terms: 6)>>
## Non-/sparse entries: 257/19
## Sparsity
: 7%
## Maximal term length: 7
## Weighting
: term frequency (tf)
##
....
We can see the eect by looking at the terms we have left:
freq <- colSums(as.matrix(dtms))
freq
##
data graham inform
time
use william
##
3101
108
467
483
1366
236
table(freq)
## freq
## 108 236 467 483 1366 3101
##
1
1
1
1
1
1
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Data Science with R
Hands-On
Text Mining
9 Identifying Frequent Items and Associations
One thing we often to rst do is to get an idea of the most frequent terms in the corpus. We use
findFreqTerms() to do this. Here we limit the output to those terms that occur at least 1,000
times:
findFreqTerms(dtm, lowfreq=1000)
## [1] "data" "mine" "use"
So that only lists a few. We can get more of them by reducing the threshold:
findFreqTerms(dtm, lowfreq=100)
##
[1] "accuraci"
"acsi"
"adr"
"advers"
"age"
##
[6] "algorithm"
"allow"
"also"
"analysi"
"angioedema"
## [11] "appli"
"applic"
"approach"
"area"
"associ"
## [16] "attribut"
"australia"
"australian" "avail"
"averag"
## [21] "base"
"build"
"call"
"can"
"care"
## [26] "case"
"chang"
"claim"
"class"
"classif"
....
We can also nd associations with a word, specifying a correlation limit.
findAssocs(dtm, "data"corlimit=0.6)
## $data
##
mine
induct
challeng
know
answer
##
0.90
0.72
0.70
0.65
0.64
##
need statistician
foundat
general
boost
##
0.63
0.63
0.62
0.62
0.61
##
major
mani
come
....
If two words always appear together then the correlation would be 1.0 and if they never appear
together the correlation would be 0.0. Thus the correlationis a measure of how closely associated
the words are in the corpus.
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Data Science with R
Hands-On
Text Mining
10 Correlations Plots
accuraci
acsi
adr
advers
age
algorithm
allow
also
analysi
angioedema
appli
applic
approach
area
associ
attribut
australia
australian
avail
averag
base
build
call
can
care
case
chang
claim
class
classif
classifi
cluster
collect
combin
common
compar
comput
condit
confer
consid
consist
contain
cost
csiro
current
data
databas
dataset
day
decis
plot(dtm,
terms=findFreqTerms(dtm, lowfreq=100)[1:50],
corThreshold=0.5)
Rgraphviz (Hansenetal.,2016) from the BioConductor repository for R (bioconductor.org) is
used to plot the network graph that displays the correlation between chosen words in the corpus.
Here we choose 50 of the more frequent words as the nodes and include links between words
when they have at least a correlation of 0.5.
By default (without providing terms and a correlation threshold) the plot function chooses a
random 20 terms with a threshold of 0.7.
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Data Science with R
Hands-On
Text Mining
11 Correlations Plot|Options
accuraci
acsi
adr
advers
age
algorithm
allow
also
analysi
angioedema
appli
applic
approach
area
associ
attribut
australia
australian
avail
averag
base
build
call
can
care
case
chang
claim
class
classif
classifi
cluster
collect
combin
common
compar
comput
condit
confer
consid
consist
contain
cost
csiro
current
data
databas
dataset
day
decis
plot(dtm,
terms=findFreqTerms(dtm, lowfreq=100)[1:50],
corThreshold=0.5)
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Data Science with R
Hands-On
Text Mining
12 Plotting Word Frequencies
We can generate the frequency count of all words in a corpus:
freq <- sort(colSums(as.matrix(dtm)), decreasing=TRUE)
head(freq, 14)
##
data
mine
use
pattern
dataset
can
model
##
3101
1446
1366
887
776
709
703
##
cluster algorithm
rule
featur
set
tree
method
##
616
611
609
578
555
547
544
wf
<- data.frame(word=names(freq), freq=freq)
head(wf)
##
word freq
## data
data 3101
## mine
mine 1446
## use
use 1366
## pattern pattern 887
## dataset dataset 776
....
We can then plot the frequency of those words that occur at least 500 times in the corpus:
library(ggplot2)
subset(wf, freq>500)
%>%
ggplot(aes(word, freq))
+
geom_bar(stat="identity")
+
theme(axis.text.x=element_text(angle=45hjust=1))
0
1000
2000
3000
algorithm
can
cluster
data
dataset
featur
method
mine
model
pattern
rule
set
tree
use
word
freq
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Data Science with R
Hands-On
Text Mining
13 Word Clouds
effect
consider
govern
adr
compon
locat
care
environ
scienc
segment
time
wholeminer
move
defin
begin
taxat
end
varieti element
complex
premium
databas
right
market
can
result
three
includ
mean
thus
architectur
term
simpl
frequent
claim
call
stream
angioedema
donohostahel
individu
threshold
mml
monitor
report
howev
base
due
observ
advers
normal
without
diseas
esophag
press
review
classifi
output
advanc
instal
transform
set
tar
outlier
entiti
theori
interv
kdd
two
known
high
caa
knowledg
hidden
induct
basic
direct
conclus
access
rattl
rather
distinct
dataset
less
receiv
month
busi
start
larger
methodolog
show
behaviour
day
new
parallel
introduct
visual
technolog
spot
demonstr
will
version
increas
csiro
respons
machin
geograph
split
type
subsequ
count
var
discov
anoth
variabl
respect
pmml
sequenc
sampl
unexpect
interpret
oper
exist
consid
partit
purpos
visualis
run
typic
forward
year
node
limit
linux
distribut
hot
act
episod
help
author
acsi
mani
consequ
predict
ensembl
seri
ace
item
now
illustr
calcul
usag
insur
case
list
region
gis
multipl
specif
difficult
breiman
log
intellig
line
understand
appli
design
issu
condit
paramet
sinc
general
detect
confer
femal
input
next
various
map
success
rank
made
intrus
algorithm
refer
provid
ann
neural
tree
subspac
mathemat
within
form
record
graphic
http
deriv
project
confid
group
event
utar
overal
smyth
engin
determin
period
upon
reduc
vector
valu
key
much
weight
matrix
tradit
tabl
recent
proport
exposur
employ
introduc
divers
attribut
categor
drug
vol
approach
context
data
user
becom
summari
hadi
queensland
distanc
probabl
hazard
problem
tmip
altern
rnn
health
occur
even
portfolio
quit
plot
network
journal
inform
system
interest
proceed
error
higher
worker
frequenc
layer
real
exclus
candid
cost
plane
factor
custom
addit
alendron
cluster
hepat
complet
focus
common
build
deploy
postdischarg
control
gnu
allow
appear
definit
evalu
descript
smaller
averag
stage
studi
discoveri
subset
patient
occurr
publish
represent
describ
advantag
age
interfac
differ
random
futur
one
therefor
open
make
medic
compar
use
william
som
choos
prepar
supp
still
optim
admiss
discuss
chen
million
natur
artifici
preprocess
pattern
offic
score
softwar
minimum
fit
must
strength
ieee
generat
practic
view
degre
doctor
target
prior
previous
gender
outcom
cover
scheme
rule
train
hybrid
code
page
area
fig
situat
assess
categori
statist
prune
part
tempor
window point
program
found
produc
suitabl
mbs
characterist
popul
mine
match
research
standard
pbs
dimension
hospit
separ
medicar
togeth
singl
forest
address
way
nation
collect
servic
posit
built
densiti
techniqu
order
applic
tool
miss
depend
method
local
analysi
consist
relat
four
expect
link
comparison
grow
accord
experi
regress
rang
australia
exampl
suppa
final
mutarc
risk
expert
certain
model
leverag
copyright
number
step
creat
residualleverag
structur
interesting
actual
extract
connect
possibl
graham
highlight
length
lead
class
continu
australian
contain
acm
import
deliv
specifi
decis
domain
clinic
remain
field
univers
test
estim
first
see
learn
action
export
implement
major
shown
cart
yes
select
nugget
industri
evolutionari
second
captur
insight
languag
literatur
interact
avail
piatetskyshapiro
suggest
size
also
debian
administr
manag
note
laboratori
unit
object
state
chang
experiment
find
messag
explor
space
follow
independ
templat
intern
patholog
work
total
like
transact
rare
investig
drg
ratio
spatial
function
give
usual
simpli
reaction
tune
activ
valid
goal
sever
propos
correspond
reason
good
huang
appropri
clear
correl
prototyp
aim
initi
file
cca
inhibitor
global
accuraci
current
fraud
support
least
develop
may
packag
obtain
detail
need
remov
commonwealth
involv
task
best
equat
particular
paper
might
section
process
emerg
benefit
rate
public
comput
preadmiss
well
abl
regular
indic
among
polici
better
present
figuroften
given
larg
identifi
associ
chosen
igi
concept
origin
similar
abstract
effici
idea
organis
classif
text
sourc
measur
world
canberra
low
perform
main
top
gain
automat
repres
construct
analys
search
framework
signific
level
featur
sequenti
requir
improv
combin
small
take
gap
We can generate a word cloud as an eective alternative to providing a quick visual overview of
the frequency of words in a corpus.
The wordcloud (?) package provides the required function.
library(wordcloud)
set.seed(123)
wordcloud(names(freq), freq, min.freq=40)
Notice the use of set.seed() only so that we can obtain the same layout each time|otherwise
arandom layout is chosen, which is not usually an issue.
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2013-2015 Graham@togaware.com
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Data Science with R
Hands-On
Text Mining
13.1 Reducing Clutter With Max Words
provid
servic
figur
case
patient
databas
popul
valu
dataset
distribut
data
perform
problem
knowledg
function
describ
mine
develop
record
process
also
drug
risk
number
first
paper
can
discoveri
one
includ
user
kdd
cluster
map
research
inform
statist
generat
tree
forest
learn
random
sourc
may
associ
comput
identifi
present
system
decis
studi
measur
support
tabl
group
sequenc
algorithm
time
class
detect
health
interest
approach
analysi
relat
select
set
variabl
two
techniqu
larg
base
structur
william
classif
event
period
similar
new
section
tempor
differ
test
rule
insur
method
pattern
model
high
will
outlier
mani
work
train
applic
general
use
exampl
result
featur
To increase or reduce the number of words displayed we can tune the value of max.words=. Here
we have limited the display to the 100 most frequent words.
set.seed(142)
wordcloud(names(freq), freq, max.words=100)
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2013-2015 Graham@togaware.com
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Data Science with R
Hands-On
Text Mining
13.2 Reducing Clutter With Min Freq
larg
evalu
import
random
like
section
understand
includ
subset
method
individu
perform
combin
step
event
work
attribut
select
form
averag
year
effect
univers
class
link
repres
call
reaction
support
function
base
user
explor
variabl
measur
rank
inform
tabl
statist
studi
analysi
area
distanc
tool
level
outlier
servic
experi
time
data
search
cost
william
subspac
patient
show
implement
make
similar
databas
consist
record
lead
unexpect
exist
propos
utar
angioedema
tempor
pattern
stage
intellig
figur
distribut
condit
result
discuss
accuraci
drug
australia
identifi
domain
appli
hospit
general
mani
number
period
paper
case
featur
build
process
describ
hot
technolog
sequenc
ratio
insur
day
well
given
kdd
use
rnn
order
within
node
target
applic
two
chang
discov
error
total
transact
small
graham
acsi
fig
need
expect
often
learn
multipl
structur
interest
current
claim
new
detect
journal
observ
report
allow
size
one
adr
provid
approach
high
can
find
classif
window
weight
model
compar
neural
differ
mine
requir
three
australian
particular
vector
sourc
health
singl
indic
entiti
system
valu
common
advers
pmml
occur
cluster
exampl
state
increas
howev
rattl
task
hybrid
usual
network
defin
mean
regress
unit
dataset
rule
visual
relat
algorithm
follow
page
intern
http
type
point
expert
proceed
effici
machin
open
avail
collect
object
csiro
discoveri
train
generat
scienc
map
consid
confer
may
tree
packag
refer
sampl
will
group
forest
popul
also
polici
comput
interesting
first
oper
episod
associ
problem
medic
decis
set
interv
signific
predict
classifi
research
contain
knowledg
risk
present
develop
care
nugget
techniqu
estim
test
age
spot
Amore common approach to increase or reduce the number of words displayed is by tuning the
value of min.freq=. Here we have limited the display to those words that occur at least 100
times.
set.seed(142)
wordcloud(names(freq), freq, min.freq=100)
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2013-2015 Graham@togaware.com
Module: TextMiningO
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